U.S. patent application number 13/665323 was filed with the patent office on 2013-05-02 for expert system to facilitate source localization of brain electrical activity.
This patent application is currently assigned to CASE WESTERN RESERVE UNIVERSITY. The applicant listed for this patent is Case Western Reserve University. Invention is credited to Mehdi Bagheri-Hamaneh, Farhad Kaffashi, Samden Lhatoo, Ken Loparo, Hans Luders, John Turnbull.
Application Number | 20130109996 13/665323 |
Document ID | / |
Family ID | 48173100 |
Filed Date | 2013-05-02 |
United States Patent
Application |
20130109996 |
Kind Code |
A1 |
Turnbull; John ; et
al. |
May 2, 2013 |
EXPERT SYSTEM TO FACILITATE SOURCE LOCALIZATION OF BRAIN ELECTRICAL
ACTIVITY
Abstract
A system or method can facilitate source localization of brain
electrical activity. In one example, an expert system can be
utilized to evaluate the source localization results and, based on
the acceptability of the results, adjust a data preparation phase
to provide for acceptable source localization.
Inventors: |
Turnbull; John; (Westlake,
OH) ; Bagheri-Hamaneh; Mehdi; (Hudson, OH) ;
Kaffashi; Farhad; (Cleveland Heights, OH) ; Luders;
Hans; (Chagrin Falls, OH) ; Loparo; Ken;
(Chesterland, OH) ; Lhatoo; Samden; (Shaker
Heights, OH) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Case Western Reserve University; |
Cleveland |
OH |
US |
|
|
Assignee: |
CASE WESTERN RESERVE
UNIVERSITY
Cleveland
OH
|
Family ID: |
48173100 |
Appl. No.: |
13/665323 |
Filed: |
October 31, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61553688 |
Oct 31, 2011 |
|
|
|
Current U.S.
Class: |
600/544 |
Current CPC
Class: |
A61B 5/7264 20130101;
A61B 5/7425 20130101; A61B 5/04012 20130101; A61B 5/4094 20130101;
A61B 5/04017 20130101; A61B 5/0476 20130101; A61B 5/726 20130101;
A61B 5/7203 20130101; A61B 5/743 20130101; A61B 5/0035 20130101;
A61B 5/0042 20130101 |
Class at
Publication: |
600/544 |
International
Class: |
A61B 5/04 20060101
A61B005/04; A61B 5/00 20060101 A61B005/00; A61B 5/0476 20060101
A61B005/0476 |
Claims
1. A computer-implemented system to localize a source of electrical
activity in a patient's body, the system comprising: a
preprocessing component programmed to preprocess electrical data
corresponding to electrical signals acquired from a patient; an
inverse model to represent the source of electrical activity; a
forward model to represent geometry and electrical characteristics
of a spatial region of interest for the patient's body based on the
inverse model; a source localization function programmed to compute
a location for the source of electrical activity within the spatial
region of interest based on the preprocessed electrical data, the
inverse model and the forward model; and an expert system to
evaluate an acceptability of the computed location for the source
of electrical activity and to selectively adjust of at least one of
the preprocessing, the inverse model and the forward model based on
the evaluation of the acceptability, the expert system adapted to
cause the source localization function to re-compute the location
for the source of electrical activity based on the selective
adjustment.
2. The system of claim 1, wherein expert system further comprises:
an evaluator programmed to evaluate the acceptability of the
computed location for the source of electrical activity based on
predefined criteria; and an adjustment control programmed to
perform the selective adjustment automatically based on
preprogrammed rules applied to results of the evaluation of the
acceptability.
3. The system of claim 2, wherein the adjustment control further
comprises: a preprocessing control programmed to adjust the
preprocessing automatically based on the rules; and a model
selector programmed to choose at least one of the inverse model and
the forward model based on the rules.
4. The system of claim 2, wherein if the results of the evaluation
of the acceptability are unacceptable, the adjustment control is
programmed to selectively at least one of the preprocessing
component, the inverse model and the forward model in an order of
increasing complexity for each subsequent location that the source
localization component computes for the source of electrical
activity.
5. The system of claim 1, wherein the preprocessing further
comprises: a spectral analysis function to determine frequency
characteristics of the electrical signals; and a filter function
programmed to filter the electrical data, the expert system
selectively controlling the filter based on the frequency
characteristics based on the acceptability of the computed location
for the source of electrical activity.
6. The system of claim 1, further comprising: a noise estimation
function programmed to estimate relative noise of the of a
plurality of channels that provide the electrical signals; and a
channel control function programmed to remove the electrical data
corresponding to noisy channels based on the relative noise, the
expert system selectively controlling the channel control function
to remove the noisy channels from the electrical data based on the
acceptability of the computed location for the source of electrical
activity, the source localization function to re-computes the
location for the source of electrical activity without electrical
data corresponding the noisy channels.
7. The system of claim 1, wherein the expert system is further
programmed to modify the forward model and provide a modified
forward model based on the acceptability of the computed location
for the source of electrical activity, the source localization
function to re-compute the location for the source of electrical
activity based on the modified forward model.
8. The system of claim 1, wherein the expert system is further
programmed to modify the inverse model and provide a modified
inverse model based on the acceptability of the computed location
for the source of electrical activity, the source localization
function to re-compute the location for the source of electrical
activity based on the modified inverse model.
9. The system of claim 1, wherein the preprocessing component
further comprises: a component analysis function programmed to
separate artifacts from cerebral electrical activity; a transform
to detect the artifacts separated from the cerebral electrical
activity by the component analysis function, the detected artifacts
being removed from the electrical signals acquired from the patient
to provide artifact-removed electrical data, and the source
localization function to re-compute the location for the source of
electrical activity based on based on the artifact-removed
electrical data.
10. The system of claim 1, further comprising a selection function
programmed to select an interval corresponding to the electrical
activity that is to be localized, the source localization function
to compute the location for the source of electrical activity based
on the electrical data residing in the selected interval.
11. The system of claim 1, further comprising an output generator
programmed to generate an output representing the computed location
for the source of electrical activity in a graphical representation
of the patient's body.
12. The system of claim 1, wherein the spatial region of interest
and the source of electrical activity reside in the patient's
brain, and the electrical activity being localized corresponds to a
spike in brain electrical activity.
13. A non-transitory computer-readable medium having instructions
which, when executed, cause a processor to: prepare brain
electrical data acquired from a patient and geometry data; perform
source localization based on the prepared brain electrical data and
geometry data to determine a location of a source of electrical
activity; implement an expert system to evaluate an acceptability
of the source localization and to adjust at least one of the
preparation of the brain electrical data and the geometry data if
the acceptability of the source localization is not within a
predetermined acceptability level; and store output data
corresponding to the location of the source of electrical activity
if the acceptability of the source localization is within the
predetermined acceptability level.
14. The computer-readable medium of claim 13, wherein the
preparation of the brain electrical data includes preprocessing of
the electrical data acquired for the patient, the expert system
being programmed to adjust a parameter of the preprocessing to
provide modified preprocessing based on the acceptability of the
source localization, the source localization being re-performed
based on the modified preprocessing.
15. The computer-readable medium of claim 14, wherein preprocessing
includes a filter, the expert system being further programmed to
adjust at least one parameter of the filter and provide an adjusted
filter based on frequency analysis of the brain electrical data,
the source localization being re-performed based on modified
prepared electrical data that is generated based on the adjusted
filter.
16. The computer-readable medium of claim 14, wherein the expert
system is further programmed to cause noisy channels to be removed
from the brain electrical data based on a relative noise analysis
of input channels for the brain electrical data, the source
localization being re-performed based on the prepared electrical
data and geometry data with the noisy channels removed from the
brain electrical data.
17. The computer-readable medium of claim 13, wherein the
preparation of the brain electrical data employs a forward model,
the expert system is further programmed to modify the forward model
and provide a modified forward model based on the acceptability of
the source localization, the source localization being re-performed
based on the modified forward model.
18. The computer-readable medium of claim 13, wherein the
preparation of the brain electrical data employs an inverse model,
the expert system is further programmed to modify the inverse model
and provide a modified inverse model based on the acceptability of
the source localization, the source localization being re-performed
based on the modified inverse model.
19. The computer-readable medium of claim 13, wherein the geometry
data comprises image data for the patient that is registered with
electrode location data determined for each of a plurality of
electrodes.
20. The computer-readable medium of claim 19, wherein the
instructions are further operative to select a temporal interval of
electrical signals acquired by the plurality of electrodes, the
temporal interval of electrical signals containing electrical
activity to be localized, the source localization being performed
to compute a location for a source of the electrical activity based
on the prepared brain electrical data and geometry data, an inverse
model and a forward model.
21. The computer-readable medium of claim 13, wherein the
preparation of the brain electrical data further comprises
instructions to: perform component analysis on the brain electrical
data to separate artifacts from brain electrical activity; and
detect the artifacts separated from the brain electrical activity,
the detected artifacts being removed from the brain electrical data
acquired from the patient to provide artifact-removed electrical
data, the source localization being re-performed based on the
artifact-removed electrical data and the geometry data.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the benefit of U.S. Provisional
Patent Application Ser. No. 61/553,688, filed Oct. 31, 2011, and
entitled SOURCE LOCALIZATION OF BRAIN ELECTRICITY ACTIVITY, the
contents of which is incorporated herein by reference in its
entirety.
TECHNICAL FIELD
[0002] This disclosure relates to source localization of brain
electrical activity.
BACKGROUND
[0003] Electroencephalography (EEG) is the recording of electrical
activity along the scalp. EEG measures voltage fluctuations
resulting from ionic current flows within the neurons of the brain.
It is desirable to determine the source of certain electrical
activity that is discernable from the EEG. For example, EEGs are
utilized as part of diagnosis and surgical treatment planning for
patients that may suffer from focal epilepsy. The process of
finding the location and distribution of the sources that create
these activities is called electrical source imaging (ESI) or
source localization. However, existing source localization methods
using EEG tend to be overly complicated for the user when accurate
estimates are required.
[0004] Magnetoencephalography (MEG) is a technique for mapping
brain activity by recording magnetic fields produced by electrical
currents occurring naturally in the brain. MEG can be used for
source localization, such by calculating a set of equivalent
current dipoles. While MEG may often provide increased accuracy
relative EEG, the underlying technology is much more expensive and
not as readily available.
SUMMARY
[0005] This disclosure relates to source localization of electrical
activity.
[0006] As one example, a computer-implemented system can localize a
source of electrical activity in a patient's body. The system can
include a preprocessing component programmed to preprocess
electrical data corresponding to electrical signals acquired from a
patient. An inverse model (e.g., stored in memory) can represent
the source of electrical activity. A forward model (e.g., also
stored in memory) can represent geometry and electrical
characteristics of a spatial region of interest for the patient's
body based on the inverse model. A source localization function can
be programmed to compute a location for the source of electrical
activity within the spatial region of interest based on the
preprocessed electrical data, the inverse model and the forward
model. An expert system can evaluate an acceptability of the
computed location for the source of electrical activity and to
selectively adjust of at least one of the preprocessing, the
inverse model and the forward model based on the evaluation of the
acceptability. The expert system can also be adapted to cause the
source localization function to re-compute the location for the
source of electrical activity based on the selective
adjustment.
[0007] As another example, a non-transitory computer-readable
medium having instructions which, when executed, can cause a
processor to prepare brain electrical data acquired from a patient
and geometry data. Source localization can be performed based on
the prepared brain electrical data and geometry data to determine a
location of a source of electrical activity. An expert system can
be implemented to evaluate an acceptability of the source
localization and to adjust at least one of the preparation of the
brain electrical data and the geometry data if the acceptability of
the source localization is not within a predetermined acceptability
level. Output data, corresponding to the location of the source of
electrical activity, can be stored if the acceptability of the
source localization is within the predetermined acceptability
level.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 depicts an example of a system to facilitate
localizing brain electrical activity.
[0009] FIG. 2 depicts another example of a system for source
localization.
[0010] FIG. 3A depicts an example of artifact contaminated
signals.
[0011] FIG. 3B depicts an example of artifacts extracted from the
signals of FIG. 3A.
[0012] FIG. 4 depicts an example of wavelet transformation being
used to facilitate artifact detection.
[0013] FIG. 5 depicts an example of the signals of FIG. 3a after
implementing artifact removal.
[0014] FIG. 6 depicts an example graphical user interface
demonstrating EEG signals.
[0015] FIG. 7 depicts an example of a graphical user interface
demonstrating source localization on an image.
[0016] FIGS. 8A and 8B collectively depict a flow diagram
demonstrating an example of a method that can be implemented to
facilitate source localization.
[0017] FIG. 9 depicts an example of a method to generate electrode
location data.
[0018] FIG. 10 depicts an example of a graphical user interface to
facilitate digitization of electrode locations.
[0019] FIG. 11 depicts an example computing environment.
DETAILED DESCRIPTION
[0020] This disclosure provides systems and methods that can be
used to facilitate source localization of electrical activity for
the brain. The source localization can be reconstructed and
represented in a graphical form, such as superimposed in a brain
image (e.g., an MRI). In one example, an expert system can be
utilized to evaluate the source localization results and, based on
the acceptability of the results, provide automatic adjustments
during a data preparation phase to achieve acceptable source
localization results. This approach can be implemented with an
acceptable accuracy and in the absence of more complex and
expensive methods, such as magnetoencephalography (MEG).
Additionally, the expert system affords a reduced learning curve
and sophistication to operate the system effectively while
achieving satisfactory results.
[0021] FIG. 1 depicts an example of a system 10 that can be
utilized to facilitate localizing electrical activity, such as
brain electrical activity. As mentioned above, the system 10
employs an expert system 12 to make adjustments to the data
preparation phase 14 if it is determined that the source
localization results are not acceptable. As used herein, the expert
system can be implemented as machine-readable instructions,
executable by a processor, to emulate decision-making process of a
human expert in the field of source localization. The instructions
can be stored in a non-transitory computer-readable medium (e.g.,
volatile or non-volatile memory), which can be accessed by a
processor to perform the functions disclosed herein. Examples of
some types of expert systems that might be used in the system 10
include any artificial intelligence, such as rule-based methods, an
inference engine (e.g., fuzzy logic, propositional logic, modal
logic or the like), algorithmic methods and the like.
[0022] The data preparation phase 14 can include preprocessing 16
of electrical data 18. For example, the electrical data can include
data representing EEG signals that can be acquired by an electrical
sensing system 20. The electrical sensing system 20 thus can
include an arrangement of electrodes that are distributed
non-invasively on a patient's head surface to measure brain
electrical activity. The patient can include any living being,
human or otherwise. The position and number of EEG electrodes can
vary. Each EEG electrode in the sensing system 20 corresponds to a
sensing channel that provides a signal representing sensed brain
electrical activity. As described herein, the preprocessing can
include filtering (e.g., high-pass and/or low-pass filters),
averaging, principal component analysis, independent component
analysis, and other noise and artifact reduction techniques. An
initial simplified configuration can be implemented as a default
for the preprocessing (e.g., some nominal high-pass filtering).
[0023] In order to perform, source localization for measured brain
electrical activity, the data preparation 14 can also employ
geometry data 22. The geometry data 22 can characterize geometry of
relevant anatomy. The geometry data can include actual patient
geometry, such as acquired by an imaging system 24. The imaging
system 24 can be implemented as any imaging modality (e.g.,
computed tomography (CT), magnetic resonance imaging (MRI),
Positron emission tomography (PET), or the like). For purposes of
simplification and consistency, the following discussion will often
discuss the system in the context of MRI imaging. As an alternative
to using an MRI for a given patient, the imaging system 24 can
correspond to a generic or standard MRI that provide generally
applicable anatomic information. Thus, the patient specific MRI can
provide a more realistic and individualized head model, whereas the
generic MRI can produce a general model that is used for all
patients and tends to result in less accurate localization.
[0024] The geometry data 22 can also include data representing the
position of electrodes in the electrical sensing system 20. An
electrode locator 26 can be configured to determine coordinates of
the electrodes on the patient's head. As one example, the electrode
locator 26 can employ an electromagnetic digitzer, such as the
Fastrack system available from Polhemus, Inc. of Colchester, Vt.
This and other approaches (e.g., image techniques) to localize the
electrodes can be employed to provide a relative location of
electrodes in a respective coordinate system.
[0025] A co-registration function 27 can be programmed to
co-register the coordinates, corresponding to electrode locations
determined by the electrode locator 26, with the image data from
the imaging system 24 into a common spatial coordinate system for
the head geometry (e.g., patient-specific or standard geometry).
The co-registration function 27 can be implemented as a fully
automated, semi-automated or a manual method to align the
coordinates of the electrode locations with respect to the image.
Such co-registration 27 performed on the electrode coordinates
enables the localized source to be visualized in a two-dimensional
or three-dimensional image. The image can be a specific patient
image or it may be a generic image of anatomy, such as an
anatomical atlas (e.g., an atlas brain).
[0026] The data preparation 14 also includes other components to
enable a source localization method 28 to determine a location of
an identified potential. The other components can include an
inverse model 30 and a forward model 32. The inverse model and the
forward model are used to solve the inverse problem associated with
locating a source of a known potential. The inverse problem,
however, is ill posed and does not have a unique solution.
Accordingly, the system 10 solves the "forward problem" by
computing the potential distribution due to a known source inside
the brain and then tries to find the best location, orientation,
and strength for the source that produces the closest potential
distribution to the actual measured data. This means that one has
to make some assumptions about the distribution of source (or
sources) that produce the EEG signals. These assumptions that
constrain the underlying solution correspond to the inverse model
30.
[0027] As one example, the inverse model 30 can represent a single
dipole as the source of the activity of interest. As another
example, the inverse model 30 can be implemented as including more
than one dipole (multiple-dipole model) or thousands of dipoles
(distributed models) for performing source localization. In
addition to the structure of the source, the inverse model can also
characterize temporal motion of the source. Thus, the inverse model
30 can represent assumptions about time evolution of the one or
more dipoles. The inverse model 30 can also correspond to a moving
dipole that allows both the orientation and location of the dipoles
to change with time. In a "rotating dipole" approach, the locations
of the dipoles are fixed, but their orientations are
time-dependent. A "fixed dipole" approach assumes that both
orientations and locations of the dipoles are fixed over time.
There are also several different distributed models that can be
utilized for the inverse model. As disclosed herein, the expert
system 12 can selectively employ one of a plurality of such inverse
models 30 for use in performing the source localization 28. A
single-dipole model can be a default model used for many clinical
purposes (e.g., in epilepsy), but other inverse models can be used
depending on the purpose of source localization and also depending
on the complexity of the EEG signal.
[0028] The data preparation 14 also employs the forward model 32
for source localization to represent assumptions related to
geometry and electrical properties (e.g., conductivities) for
different regions of the head. The system 10 can select one of a
plurality of different forward models for use by the source
localization 28. As one example, the forward model can model the
head as a single sphere or as a sphere having two or more shells.
For instance, a spherical model can include three spheres, such as
representing the head as consisting of three concentric spheres
(skin, skull, and brain) with different conductivities. A
four-shell spherical method adds another layer to the previous
model (e.g., cerebrospinal fluid).
[0029] By way of further example, the forward model can be
implemented as a realistic head model. Two examples of realistic
models include the boundary element method (BEM) and finite element
method (FEM). In each of BEM and FEM, the surfaces of different
regions of the head can be reconstructed using the patient's actual
MRI or using a standard MRI. In BEM, after reconstructing the
boundaries between different head regions, the surfaces can be
triangulated; i.e. they are partitioned to small triangles. In FEM,
each region is tessellated to small tetrahedral elements. The
triangulation/tessellation step is necessary for performing the
calculations needed for the forward solution. The FEM method can be
considered more accurate than BEM because it allows more accurate
reconstruction of geometry, and also because conductivity
anisotropy of skull and white matter can be taken into account in
this method. A given forward model can be utilized by the source
localization 28, which can be a default forward model or another
one of a plurality of available models. For example, during
operation, the expert system 12 can select a given forward model
from an available set of different forward models. The different
forward models further can include a plurality of different
implementations of BEM forward models and/or a plurality of
different implementations of FEM forward models.
[0030] As a further example, the forward solution (calculating the
potential due to a known source) can be performed in multiple
steps. For instance, a transfer matrix can be calculated, as part
of the data preparation 14, based on the geometry of different head
regions and their conductivities which can be utilized to provide
part of the forward model 32. Since the transfer matrix is not
dependent on the electrical (e.g. EEG) signal (and only depends on
the head parameters), it can be calculated only once for each
patient for a given set of electrical measurements. This is
important because this calculation may take a while, especially if
FEM is chosen as the forward model, due to the complexity of the
computations. The calculated transfer matrix can then be utilized
by the source localization 28 to calculate the potential fitted to
the recorded electrical data 18. The potential can be either be
pre-computed at this stage and implemented as part of the forward
model, such as for a pre-determined mesh of dipoles. Alternatively,
the potential can be computed and evaluated by the source
localization 28 at the time of fitting. If the pre-computation
approach is utilized, the results can be stored in memory and later
compared to the actual data to ascertain the localization
results.
[0031] The source localization 28 is programmed to localize one or
more sources of electrical potential, such as a selected spike.
There are different approaches that can be utilized for source
localization, which can vary based on the inverse model 30 and the
forward model 32 that are utilized. The source localization 28 can
compute the location of the source(s) based on the preprocessed
electrical data (e.g., provided by the processing component 16),
the inverse model 30, and the forward model 32.
[0032] The source localization 28 can determine specifications of
the source that produces the closest electrical potential to the
actual recorded potential. The specifications can include a
position (e.g., coordinates) of the localized source in
three-dimensional space. The localized position can be identified
as one or more points, an area (e.g., a surface area that may be
flat or curved) or a volume (e.g., a three-dimensional region). The
corresponding type of localized position may vary based on the
inverse model that is utilized. As one example, the source
localization 28 can be programmed to fit pre-computed (and saved in
memory) potentials produced by a grid of dipoles by comparing the
pre-computed potentials to the actual electrical data 18 to find a
best solution. If the pre-computation method is utilized no fitting
is done and this step can be performed very fast, although the
pre-computation part may take longer. As another example, the
source localization can be programmed to fit the forward solution
to the recorded data by minimizing the difference (e.g., via a
least squares method). The latter approach tends to be slower than
pre-computing potentials, but would decrease the amount of time
spent on pre-computation. The approach utilized can be selected by
the user, or be determined by the expert system.
[0033] The expert system 12 can include an evaluator 34 programmed
to evaluate the acceptability of the results provided by the source
localization 28. The evaluator 34 can ascertain the acceptability
of the localization results by employing one or more evaluation
criteria. If the results of the source are not acceptable, an
adjustment function can used to modify the data preparation process
14 and the source localization can re-compute the solution
according to the adjustments.
[0034] As one example, the evaluator 34 can be programmed to
compute a goodness of fit (GOF) that provides a measure of how well
the electrical potential of the calculated source(s) matches the
actual potential. For instance, the GOF can be computed as a number
between 0 and 1, such that a GOF of 1 means a perfect fit. However,
since the data always includes noise even a perfect fit may not
guarantee that the true geometric location of the source has been
found. A threshold can be set so that a GOF above the threshold
indicates that the localized source is acceptable and below such
threshold is unacceptable.
[0035] Additionally or alternatively, the evaluator 34 can be
programmed to compute a confidence region. The confidence region,
in contrast to the GOF, depends on the noise level and is defined
as the region that, with a certain probability, contains the
source. The evaluator 34 can compute a volume of the confidence
region as a good indicator of the EEG noise level, in which a
higher noise means larger confidence region. A threshold can be set
for a volume confidence region such that a volume computed for a
confidence region that exceeds the confidence threshold indicates
that the solution is rejected as unacceptable. If the evaluator 34
determines that the volume computed for a confidence region is less
than the threshold volume, the localized source is acceptable.
[0036] The evaluator 34 can employ one or more of the GOF, the
confidence regions as well as other methods to ascertain the
acceptability of each solution determined by the source
localization 28. As one example, the determination can be computed
as a weighted sum of both the GOF and computed confidence region.
As another example, the criteria can be selected by the user
manually, such as via the GUI 42, or automatically, such as by the
expert system 12. If the evaluator 34 determines (e.g., based on
one or more criteria as disclosed herein) that the solution for the
source localization is acceptable, the specification for the source
location can be provided to an output generator 38 to provide a
corresponding output 40. The output generator 38 can be programmed
to generate the output 40 based on the results of the source
localization 28.
[0037] As one example, a graphical (and/or text-based)
representation of the localized source can be superimposed on a
graphical representation of the patient's brain, such as provided
by the imaging system 24 (e.g., by displaying the location of the
source localization superimposed on one or more images from a
patient's MRI). For example, the localized source can be spatially
represented on the anatomical image at the location determined to
be acceptable. Where multiple acceptable localized sources have
been computed, each source can be visualized concurrently or
separately. For instance, a user can select (e.g., via GUI) a given
localized source from a list of computed sources to cause a
representation of the given localized source to be rendered
graphically in the image. If multiple localized sources are
presented concurrently in the same image, color coding or other
means of differentiating them can be utilized.
[0038] If the evaluator 34 determines (e.g., based on one or more
criteria as disclosed herein) that the solution for the source
localization 28 is not acceptable, the expert system 12 can employ
one or more adjustment functions 36 to modify one or more aspects
of the data preparation to repeat some or all of the calculations.
The adjustment function 36 can adjust one or more of the
preprocessing 16 on the electrical data 18, change the inverse
model 30 and/or change the forward model 32. The source
localization 28 thus can be recomputed for the adjusted data
preparation 14, and the adjustments and computations can be
repeated until at least one acceptable source localization is
computed for a given instance of the data preparation stage.
[0039] In other examples, the source localization 28 can be
programmed (e.g., in response to a user input) to ascertain
multiple acceptable source localizations. For instance, the expert
system 12 can adjust the data preparation phase 14, such as
disclosed above, and the source localization and compute a
corresponding source localization that is acceptable, which results
can be stored in memory. The expert system can further adjust the
data preparation phase 14 and another source localization can be
computed. This can be repeated any number of two or more times to
provide a results set of location coordinates for each acceptable
source localization result. These results can be provided to the
output generator for visualization on an anatomical image. As an
example, since each acceptable source location is computed for the
same selected signal, the different acceptable source localizations
can be aggregated statistically (e.g., a median location) to
provide a resulting source localization that can be provided in the
output 40 for the user. As another example, a user can compare the
acceptable results for each of a plurality of different data
preparation parameter sets.
[0040] FIG. 2 demonstrates another example of a system 46 to
facilitate computing an acceptable solution for source localization
of a voltage potential. The system can operate on input data 48,
such as can corresponding to the electrical data 18 and geometry
data 22 of FIG. 1. The system 46 can include a selection component
50 programmed to select which of the input data 48 is to be
utilized for source localization. The selection 50 can be
automated, semi-automated, or be manual in response to a user input
(e.g., selecting one or more signal interval for which source
localization is to be performed).
[0041] The system 46 includes a preparation stage 52, which
includes preprocessing 54, an inverse model 56 and a forward model
58. A source localization method 60 is programmed to compute a
location for a source of a selected electrical signal (e.g.,
voltage potential) based on the preprocessed electrical data,
inverse model 56 and forward model 58. For instance, the
preprocessing 54, inverse model 56 and forward model 58 can be
instantiated to defaults for use by the source localization 60 to
compute a solution.
[0042] An expert system 62 is configured to control the preparation
stage 52 for source localization 60 to facilitate computing an
acceptable solution, such as disclosed herein. One or more of the
preprocessing 54, inverse model 56 and forward model 58 can be
programmable by the expert system 62. The expert system 62 includes
an evaluation function 64 and adjustment control 66 programmed to
adjust the preparation stage 52 if the solution computed by the
source localization method 60 is unacceptable. An example algorithm
that can be implemented by the expert system 62 for evaluating and
adjusting the data preparation for performing source localization
is shown in FIGS. 8A and 8B. Other methods and artificial
intelligence can be utilized adjust selected parameters of the
preprocessing, forward model and inverse model.
[0043] In the example of FIG. 2, the evaluation function 64 can
include an acceptability evaluation 68 programmed to ascertain the
acceptability of the solution computed by the source localization
60. The evaluation function 64 can also include evaluation criteria
70 that define what constitutes an acceptable or unacceptable
solution. The criteria 70 can vary based on the technique or
combination of techniques utilized for ascertaining acceptability.
As an example, the acceptability evaluation 68 can calculate a GOF,
a confidence region or a combination thereof for the calculated
source localization. The acceptability evaluation 68 thus can
compute a metric or a combination of acceptability metrics with the
criteria 70 setting corresponding thresholds for acceptability. The
thresholds for acceptability can be user programmable or
predetermined default thresholds can be used.
[0044] As disclosed herein, the adjustment control 66 can adjust
one or more portions of the preparation stage 52 if the evaluation
function determines that the source localization solution is not
acceptable. The adjustment control 66 can employ rules 72
programmed to specify what one or more portions of the preparation
stage 52 should be adjusted. The amount and type of adjustment can
be determined by the adjustment control 66, by analysis methods in
the preparation stage 52 or such determinations can be distributed
between the adjustment control and the preparation stage. For
example, the rules 72 can be programmed to specify a type of
adjustment and the preparation stage 52 can determine the amount of
adjustment that is to be implemented.
[0045] In the example of FIG. 2, the adjustment control 66 includes
a preprocessing control 74 and a model selector 76. The adjustment
control 66 can employ the preprocessing control 74 to adjust one or
more preprocessing parameter of the preprocessing 54 based on the
rules 72. The adjustment control can also specify the amount of
adjustment or the amount can be a predetermined incremental amount,
for example. The model selector 76 can select a given forward model
from a plurality of available models (e.g., stored as a library of
forward models) 58 and a particular implementation thereof based on
the rules 72. The model selector 76 can also select the inverse
model from a plurality of available inverse models (e.g., stored as
a library of inverse models) 56 and a particular implementation
thereof based on the rules 72.
[0046] By way of further example, if the default settings for the
preparation stage do not provide an acceptable solution for the
source localization function 60, the preprocessing control 74 can
instruct the preprocessing 54 to adjust one or more parameters of a
filter 80 programmed to filter the sensed electrical signals. The
filter 80, for example, can include a low-pass filter, a high-pass
filter, a notch filter or a complex filter having an arrangement of
poles and zeros. The same filter can be applied as a common filter
to each of the input channels or different filter characteristics
can be applied selectively to each channel. The preprocessing 54
can include a spectral analysis function 78 programmed to determine
frequency characteristics of the sensed signals, including the
particular signal segment that was selected for source
localization, and adjust the filter 80 based on the frequency
characteristics. For example, the filter 80 can be adjusted to
remove unwanted signals from the activity of interest to improve
the signal to noise ratio. The source localization function 60 can
re-compute the solution with the modified instance of the filter
80.
[0047] If the evaluation function 64 determines that the solution
is still unacceptable, the preprocessing control 74 can instruct a
noise estimation function 82 of the preprocessing 54 to estimate
the noise for each of the input channels. If such calculation
demonstrates that the noise for one or more channels exceeds the
noise for other channels by a predetermined amount (e.g., relative
noise per channel), channel control 86 can remove such noisy
channels and the source localization 60 can be repeated without
such noisy channels. Additionally, or alternatively, if the noise
estimation function 82 determines that the noise for one or more
channels exceeds a noise level, but not a relative noise threshold,
the preprocessing 54 can employ other noise reduction techniques 84
based on the characteristics of the noise that is detected.
[0048] As a further example, the preprocessing control 74 can
determine that different (e.g., more sophisticated) types of noise
reduction may be required based on application of the rules 72. For
example, the preprocessing 54 can also apply principal or
independent component analysis (PCA or ICA) 88 to detect and remove
artifacts that can occur during signal acquisition. The component
analysis 88 can include PCA and/or ICA methods. For example,
artifacts can occur in electrical (e.g., EEG) signals due to
environmental stimulations, such as audible noise and visual
stimulation as well as patient actions, such as movement (blinking,
scratching or the like).
[0049] By way of example, component analysis, such as ICA, can be
employed to separate these artifacts from the cerebral activity.
The artifacts can then automatically detected using time-frequency
analysis (e.g., continuous or discrete wavelet transformation)
and/or by determining the distribution of electrical potential due
to that component on the surface of the head. For instance, some
artifacts, such as eye blinks and heart electrical activity (e.g.,
EKG), are known to have a specific distribution on the scalp. Any
component that is determined shows a strong correlation with such
known distribution can be tagged as a possible artifact.
[0050] FIGS. 3A and 3B depict examples of EEG signals 90 and 91to
help demonstrate some effects of artifacts and how by separating
such artifacts, they can be removed from the acquired EEG signal.
Many artifacts, including eye blinks and EKG, also have a specific
morphology. EKG artifacts, for example, are spiky and periodic,
such as shown in the EEG signals contaminated with EKG artifact in
the example of FIG. 3A. Applying a wavelet transformation to such
signal component can produce a signal with much higher peaks to
facilitate detection and removal of the peaks corresponding to such
artifacts. In the example of FIG. 3A, an EKG-induced peak,
demonstrated at 93, occurs nearly concurrently as an epileptic
spike that needs to be source localized. If not removed, the
artifact could alter the source localization result. As
demonstrated in the example of FIG. 3B, ICA can be used to separate
different components of the signal. The signals 91 in FIG. 3B thus
correspond to independent components computed using ICA on the
signals 90 from FIG. 3A.
[0051] A continuous wavelet transformation (e.g., using
Coiflet-type wavelets) can be applied to all separated components
using ICA. This transformation thus can enhance the peaks of the
EKG-related component, such as to facilitate their detection. As a
further example, FIG. 4 demonstrates an example of a wavelet
transformation 94 that can be applied to the ICA-separated signals
(e.g., the signals 91 of FIG. 3B) to detect corresponding
components thereof, demonstrated at 95. The next step is to check
that the peaks occur periodically such that the periodically
occurring components can be removed by subtracting such frequency
domain components from the wavelet transformation of the acquired
signals.
[0052] Once the artifact is found, as mentioned above, it is
rejected (e.g., by subtracting the components from the original
signal in the transform domain). The resulting artifact-free
signals are reconstructed (e.g., via inverse wavelet
transformation). FIG. 5 demonstrates an example of EEG signals 96
that can be generated from acquired signals 90 of FIG. 3A based on
preprocessing the signals by performing ICA, wavelet transformation
and inverse transformation, as disclosed above. The source
localization 60 of FIG. 2 can proceed on the reconstructed
artifact-free signals, as disclosed herein. Similar algorithms can
be used for detection and removal of other artifacts, such as eye
blinks or the like. The artifact detection and removal can be fully
automated. For example, the artifact detection and removal can be
activated continuously. Alternatively, in other examples, artifact
removal can be activated (or deactivated) in response to a user
input, such as via activating a GUI element.
[0053] As used herein, artifact-free indicates that artifacts have
been removed; however, it does not require 100% removal but rather
simply improved content. Additionally, the artifact removal further
can focus on each signal interval of interest (selected temporally)
since artifacts temporally outside of the signal interval being
localized have little effect on source localization.
[0054] Referring back to FIG. 2, since the expert system 62
determines what preprocessing techniques to utilize and the
settings can be automatically determined, expertise of such
techniques by users is not required. When the expert system 62
determines to adjust the preprocessing 54, only the fitting for the
source localization and preprocessing of the signals need to be
repeated for each selected signal activity that is going to be
source localized.
[0055] The expert system 62 can also be programmed to employ the
model selector 76 to selectively change one or both of the inverse
model 56 and the forward model 58 if the evaluation function
determines that the solution computed by the source localization is
unacceptable. When the forward model is changed, the pre-computed
potentials and transfer matrix should be re-computed as disclosed
herein, unless they are already computed and saved for the new
forward model. In other words, these calculations are pre-computed
only once for a specific forward model 58. The preprocessing 54 can
return to its default settings for performing the source
localization. Alternatively, one or more of the filter 80, noise
reduction 84 and channel control 86 can remain at previous values
for use in preprocessing the electrical input signals for employing
the source localization function. When one or both models 56 and 58
are changed, the source localization 60 computes a solution. The
evaluation function 64 of the expert system 62 can again determine
if the solution is acceptable and, if necessary, automatically
adjust the preparation stage 52, as disclosed herein, such that
user interaction can be reduced relative to other source
localization approaches.
[0056] As disclosed herein, the systems and methods can be utilized
to source localize any number of one or more spikes from a set of
electrical data. Each such spike can be identified by the selector
76. The selector 76, for example, can select the one or more spikes
manually in response to a user input or via automated selection
methods. Since noise levels vary with time, the preprocessing 54
will be performed for each electrical signal segment that is to be
source localized by the source localization method 60.
[0057] For example, if one elects to source localize a plurality of
epileptic spikes in an EEG recording (e.g., automated or manual
spike selection), settings of the filter 80 may have to be adjusted
for each spike depending the frequency of the noise determined by
the spectral analysis function 78 for each spike. In addition to
choosing filter settings, the expert system 62 also can determine
when and how to apply component analysis 88 as well as other more
sophisticated signal preprocessing (e.g., wavelet analysis) and
noise reduction techniques. Such automated aspects via the expert
system 62 further facilitate performing accurate source
localization 60 for average users and also reduces the time, effort
and expertise required to achieve acceptable source localization
results.
[0058] As a further example, FIG. 6 demonstrates an example of a
graphical user interface 100 demonstrating part of an EEG recording
for a patient. In the example of FIG. 6, a sharp wave, indicated at
102, can be identified by the selector GUI element 104 at about
31:23 for performing source localization. Such selection of the
sharp wave 104 can be automated via signal processing or it can be
selected via a mouse or other user interface function. For example,
a GUI (e.g., corresponding to the selector of FIG. 2) can include
different source localization settings, such as to localize a
single spike, an evoked potential or an average spike.
[0059] The GUI 100 can also include a plurality of GUI elements
106, such as are demonstrated as GUI buttons. In the example of
FIG. 6 the GUI elements 106 include a settings button, a
precalculation button, a switch image button, an apply CA button
and a remove artifacts button. Each of the GUI elements 106 are
programmed to activate functions and method disclosed herein in
response to a user selecting a respective button. Additional
flexibility can be achieved using the GUI 100, such as allowing a
user the ability to selectively apply component analysis (e.g., ICA
or PCA) to the acquired signals in response to activating the Apply
CA button. Additionally or alternatively, a user can selectively
enable or disable removal artifacts in response to activating or
deactivating, respectively, the remove artifacts button. The type
of source or sources being localized (e.g., a single spike, an
evoked potential an average spike) can be selected in response to a
user input selecting the type. Unlike existing systems, which
require complicated manual adjustments to parameters and functions,
the approach disclosed herein employs an expert system to select
parameters and configure its functions to achieve acceptable source
localization results.
[0060] FIG. 7 demonstrates a screen shot of a GUI 120 that can be
implemented based on the systems and methods disclosed herein for
electrical source imaging. Similar to the example of FIG. 6, the
GUI 120 includes a list of waveform intervals that can be selected
for source localization. In the example of FIG. 7, a sharp wave at
00:31.23 is shown selected for localization. Also demonstrated is a
GUI region (e.g., a window) 124 that provides further information
about the source localization process. In the example of FIG. 7,
the GUI region 124 includes a list of different dipoles and
calculations for two acceptability criteria (e.g., GOF and volume
of the confidence region) for computations made for each dipole.
Also demonstrated in the GUI region 124 are some details about
preprocessing employed by the system for the source localization
process, such as including an indication of the high-pass filter,
inverse model and forward model. As disclosed in relation to the
GUI 100 of FIG. 6, the GUI 120 can also include the same or similar
to GUI elements (e.g., buttons) for selectively controlling some
high-level pre-processing functionality and selecting the type of
source being localized.
[0061] The GUI 120 can also display one or more anatomical images
that include an indicia (e.g., graphical and/or text) 128, 130 and
132 to identify the localized source for each selected signal
interval, such as can be selected at 122. For example, the images
can be actual images (e.g., MRI images) of the patient's brain with
a graphical representation of one or more source localized spikes
superimposed on the brain images, as demonstrated at 128, 130 and
132. In other examples, a generic anatomical image can be used to
show the results of the source localization. In either case, the
images can be one or more two-dimensional or three-dimensional
images.
[0062] As mentioned above, FIGS. 8A and 8B demonstrate an example
of a workflow method 146 that can be implemented by an expert
system (e.g., by the expert system 12 of FIG. 1 or 62 of FIG. 2) in
connection with performing source localization. The method 148
begins at 148 such as in connection with initializing variables to
starting and/or default values for the source localization process.
This can include, for example, employing default predetermined
pre-processing parameters, a default inverse model and a default
forward model.
[0063] At 150, one or more spikes can be selected. As disclosed
herein the spike selection can be an automated process or it can be
made in response to a user selection. At 152, the source
localization can be performed on the selected spike using the
default preprocessing, inverse model and forward model settings. At
154, a determination is made whether the results of the
calculations at 152 are acceptable. The acceptability determination
at 154 (and at other operations in the method 146) can be based on
computing and evaluating one or more acceptability criteria, such
as GOF, confidence region or a combination thereof. If the results
of the determination at 154 are acceptable, the method can proceed
to 156 where the method terminates and the localized source for the
spike can be stored in memory and used to provide an output, such
as disclosed herein (see, e.g., FIG. 7). If the results are not
acceptable, the method can proceed to 158.
[0064] For example, at 158 the signals can be analyzed to determine
if there is any low frequency, high amplitude background electrical
activity. If such activity is detected, the method can proceed to
160 in which the high-pass filter parameters can be adjusted to
increase the high-pass filter frequency. The source localization at
152 can then be repeated with the adjusted filter parameters. The
results of such source localization can be evaluated at 162 to
determine if the results are acceptable. If the results are
acceptable the method can end at 164. If the results, however, are
not acceptable based on the filter adjustments, the method can
proceed to 166.
[0065] At 166, noise levels can be estimated to determine and
identify if there are any noisy channels (e.g., comparing
signal-to-noise ratio relative to a noise threshold) that might
adversely affect the measurements. If one or more noisy channels
are identified at 166, the method proceeds to 168 to turn off the
identified noisy channels. The source localization calculation (at
152) can be repeated in the absence of the noisy channels. The
results of such source localization can be evaluated at 170 to
determine if the results are acceptable. If the results are
acceptable the method can end at 172. If the results are not
acceptable at 170, the method can proceed to 174. Additionally, if
no noisy channels were identified at 166, the method can similarly
proceed from 166 to 174.
[0066] At 174, the inverse model (e.g., the model 30 of FIG. 1 or
model 56 of FIG. 2) can be changed. For example, the expert system
method can include a library of inverse models, such as a single
dipole model, a multi-dipole model and distributed models, which
can be selected for performing source localization. Additionally,
for a given single or multi-dipole model, it further can be
correspond to a moving dipole, a fixed dipole or a rotating dipole.
The selection of the inverse model can be automated or be
implemented in response to a user input that identifies which
inverse model is to be utilized and then source localization 152
can be repeated for the next inverse model.
[0067] After the source localization is repeated the method
proceeds (via connector "A") to 180 of FIG. 8B to determination
whether the adjusted inverse model from 174 provides acceptable
source localization results. This process at 174 can be repeated
for any number or one or more different inverse models. If changing
the inverse model provides acceptable results, the method can end
at 182. If the results are not acceptable at 180, the method can
proceed to 184. At 184, the inverse model providing the most
acceptable results (e.g., determined from 174 and 180) can be
selected for subsequent source localization. In other examples, a
default inverse model can be utilized.
[0068] At 184, the forward model (e.g., the model 32 of FIG. 1 or
model 58 of FIG. 2) can be changed. For example, the expert system
method can include a library of forward models, such as can include
one or more spherical (e.g., 1-shell, 3-shell or 4-shell) models
and/or one or more realistic (e.g., BEM or FEM) models. After the
forward model has been changed, the source localization at 152 can
be computed for the modified forward model. The process at 184 can
be repeated for any number or one or more different forward models
or until acceptable results are achieved based on evaluation at
186. If changing the forward model provides acceptable results, the
method can end at 188. If the results are not acceptable at 186,
the method can proceed to 190. The forward model providing the most
acceptable results (e.g., determined from 184 and 186) can be
selected for subsequent source localization in the workflow. In
other examples, a default forward model can be utilized.
[0069] At 190, preprocessing can be implemented to remove
artifacts. For example, the artifact removal can employ component
analysis, such as ICA or PCA. Wavelet transform can further be used
to improve the detection and removal of artifacts, thus increasing
the clinical utility. Because EEG noise levels vary with time, the
pre-processing has to be re-done for any EEG segment that is to be
source localized. By way of example, if one wants to use ESI for 10
epileptic spikes in an EEG recording, the filter settings may have
to be changed for each spike depending on the characteristics (e.g.
dominant frequency) of the noise. The expert system can automate
the removal of artifacts, such as including, for example, what
filter settings to use or how and when to apply PCA, ICA or other
signal processing techniques. In other examples, the artifact
removal at 190 can be implemented to set artifact removal
parameters in response to user inputs.
[0070] The process at 190 can be repeated for any number or one or
more different artifact removal techniques or until acceptable
results are achieved based on evaluation at 192. If changing the
artifact removal is determined to provide acceptable results at
192, the method can end at 194. If the results are not acceptable
at 192, the method can proceed to 196. At 196, the method can
inform the user that a given spike or set of spikes for which the
method 146 has been implemented are not good candidates for source
localization.
[0071] From FIGS. 8A and 8B, it will be appreciated that the data
preparation (e.g., preprocessing and models) can be adjusted by the
expert system in an order of increasing complexity. Thus, by first
implementing less complex adjustments to achieve an acceptable
solution for source localization, the overall processing time can
be decreased. Additionally, since an expert system is employed to
decide if the result is acceptable and, if necessary, automatically
configures the preprocessing parameters and repeats the relevant
computations, user interaction and knowledge of complex signal
process and modeling techniques can be significantly reduced while
still achieving improved results.
[0072] FIG. 9 depicts an example of a method 200 to generate
electrode location data (e.g., data from locator 26 of FIG. 1). The
acquisition of electrode location data can be facilitated with use
of a GUI, such as disclosed in relation to the example GUI 250 of
FIG. 10. The example of FIG. 9 relates to determining electrode
location data from an electromagnetic digitizer or similar device;
however, other techniques can also be utilized to obtain
coordinates of the electrodes, such as using an imaging modality
(x-ray, CT or the like).
[0073] The method begins at 202 after electrodes have been placed
on the patient's body at a desired locations. For example,
placement of the electrodes can be individually or assisted by
using a cap or net that containing the electrodes. At 204, an
electrode can be identified and a measurement can be made at 206
for the identified electrode. For the example GUI 250 of FIG. 10,
the GUI can include a graphical map 252 of a skull that includes
locations of each of a plurality of electrodes for a given
configuration (e.g., a standard 10-20 or 10-10 configuration). A
display box 254 can also be provided to provide step-by-step
instructions to the user, such as including commands to start
digitization, when to measure each electrode location and the like.
At 208, after a measurement has been made at 206, the method can
include a measurement confirmation operation. For example, the GUI
can provide instructions to the user to manually confirm that user
had actually digitized the correct electrode per the instructions
presented in the display box 254. This can be done for each or for
a selected portion of the measurements. In other examples, a
confirmation response can be requested if it has been determined
that an electrode is a greater distance from its expected
location.
[0074] At 210, a determination is made whether the measurement is
for a last electrode. If the additional electrodes require
digitization measurements, the method proceeds to 212 to request
measurement of the next electrode (e.g., in the display box 254 of
FIG. 10). The process at 206-212 can be repeated until it is
determined that the last electrode has been digitized. At 214,
electrode coordinates can be computed based on the measurement data
acquired during the digitization process. At 216, a determination
is made as to whether the locations are acceptable. If the
locations are not acceptable (e.g., one or more electrode locations
being outside of expected locations relative to the other
electrodes), the method can return to 204 to repeat the process. In
other examples, the process can be implemented two times and if the
distance between the measurements for each electrode is more than a
predetermined distance (e.g., about 3 mm), the process can require
that electrode digitization process be performed an additional
time. Where the process is repeated, acceptable location
coordinates for each of the electrodes can be averaged to provide
corresponding average coordinates for performing source
localization. Once it is determined at 216 that the electrode
locations are acceptable, the method can proceed to 218 in which
the electrode coordinate data is stored for use in source
localization, as disclosed herein.
[0075] As mentioned above, FIG. 10 depicts an example GUI 250
interface to facilitate digitization of electrode locations. For a
given selected electrode configuration, a list of electrodes can be
provided at 256, such as can be utilized to indicate which
electrode is to be digitized in the map 252. For instance, the
electrode that is to be digitized can be highlighted in the list
during the process 200 of FIG. 9. Additional instructions regarding
each measurement (e.g., confirming instructions and the like) can
be provided in the display box 254. Other GUI elements (e.g.,
buttons) 258 can be provided to activate functions associated with
the digitization process, such as to start the process, remove a
measurement if a wrong electrode from the identified electrode was
incorrectly measured, or to view a report of the digitization
process.
[0076] In view of the foregoing structural and functional
description, those skilled in the art will appreciate that portions
of the invention may be embodied as a method, data processing
system, or computer program product. Accordingly, these portions of
the present invention may take the form of an entirely hardware
embodiment, an entirely software embodiment, or an embodiment
combining software and hardware, such as shown and described with
respect to the computer system of FIG. 11. Furthermore, portions of
the invention may be a computer program product on a
computer-usable storage medium having computer readable program
code on the medium. Any suitable computer-readable medium may be
utilized including, but not limited to, static and dynamic storage
devices, hard disks, optical storage devices, and magnetic storage
devices.
[0077] Certain embodiments of the invention have also been
described herein with reference to block illustrations of methods,
systems, and computer program products. It will be understood that
blocks of the illustrations, and combinations of blocks in the
illustrations, can be implemented by computer-executable
instructions. These computer-executable instructions may be
provided to one or more processors of a general purpose computer,
special purpose computer, or other programmable data processing
apparatus (or a combination of devices and circuits) to produce a
machine, such that the instructions, which execute via the
processor, implement the functions specified in the block or
blocks.
[0078] These computer-executable instructions may also be stored in
computer-readable memory that can direct a computer or other
programmable data processing apparatus to function in a particular
manner, such that the instructions stored in the computer-readable
memory result in an article of manufacture including instructions
which implement the function specified in the flowchart block or
blocks. The computer program instructions may also be loaded onto a
computer or other programmable data processing apparatus to cause a
series of operational steps to be performed on the computer or
other programmable apparatus to produce a computer implemented
process such that the instructions which execute on the computer or
other programmable apparatus provide steps for implementing the
functions specified in the flowchart block or blocks.
[0079] In this regard, FIG. 11 illustrates one example of a
computer system 300 that can be employed to execute one or more
embodiments of the invention, such as including acquisition and
processing of sensor data, processing of image data, as well as
analysis of transformed sensor data and image data associated with
the analysis of brain electrical activity. Computer system 300 can
be implemented on one or more general purpose networked computer
systems, embedded computer systems, routers, switches, server
devices, client devices, various intermediate devices/nodes or
stand alone computer systems. Additionally, computer system 300 can
be implemented on various mobile clients such as, for example, a
personal digital assistant (PDA), laptop computer, pager, and the
like, provided it includes sufficient processing capabilities.
[0080] Computer system 300 includes processing unit 301, system
memory 302, and system bus 303 that couples various system
components, including the system memory, to processing unit 301.
Dual microprocessors and other multi-processor architectures also
can be used as processing unit 301. System bus 303 may be any of
several types of bus structure including a memory bus or memory
controller, a peripheral bus, and a local bus using any of a
variety of bus architectures. System memory 302 includes read only
memory (ROM) 304 and random access memory (RAM) 305. A basic
input/output system (BIOS) 306 can reside in ROM 304 containing the
basic routines that help to transfer information among elements
within computer system 300.
[0081] Computer system 300 can include a hard disk drive 307,
magnetic disk drive 308, e.g., to read from or write to removable
disk 309, and an optical disk drive 310, e.g., for reading CD-ROM
disk 311 or to read from or write to other optical media. Hard disk
drive 307, magnetic disk drive 308, and optical disk drive 310 are
connected to system bus 303 by a hard disk drive interface 312, a
magnetic disk drive interface 313, and an optical drive interface
314, respectively. The drives and their associated
computer-readable media provide nonvolatile storage of data, data
structures, and computer-executable instructions for computer
system 300. Although the description of computer-readable media
above refers to a hard disk, a removable magnetic disk and a CD,
other types of media that are readable by a computer, such as
magnetic cassettes, flash memory cards, digital video disks and the
like, in a variety of forms, may also be used in the operating
environment; further, any such media may contain
computer-executable instructions for implementing one or more parts
of the algorithms or methods disclosed herein.
[0082] A number of program modules may be stored in drives and RAM
305, including operating system 315, one or more application
programs 316, other program modules 317, and program data 318. The
application programs and program data can include functions and
methods programmed to acquire, process and display electrical data
from one or more electrical sensors, such as shown and described
herein. The application programs and program data can include
functions and methods programmed to perform evaluation and
adjustments of data preparation, including parameter adjustment and
model selection, such as disclosed herein.
[0083] A user may enter commands and information into computer
system 300 through one or more input devices 320, such as a
pointing device (e.g., a mouse, touch screen), keyboard,
microphone, joystick, game pad, scanner, and the like. For
instance, the user can employ input device 320 to edit or modify a
domain model. These and other input devices 320 are often connected
to processing unit 301 through a corresponding port interface 322
that is coupled to the system bus, but may be connected by other
interfaces, such as a parallel port, serial port, or universal
serial bus (USB). One or more output devices 324 (e.g., display, a
monitor, printer, projector, or other type of displaying device) is
also connected to system bus 303 via interface 326, such as a video
adapter.
[0084] Computer system 300 may operate in a networked environment
using logical connections to one or more remote computers, such as
remote computer 328. Remote computer 328 may be a workstation,
computer system, router, peer device, or other common network node,
and typically includes many or all the elements described relative
to computer system 300. The logical connections, schematically
indicated at 330, can include a local area network (LAN) and a wide
area network (WAN).
[0085] When used in a LAN networking environment, computer system
300 can be connected to the local network through a network
interface or adapter 332. When used in a WAN networking
environment, computer system 300 can include a modem, or can be
connected to a communications server on the LAN. The modem, which
may be internal or external, can be connected to system bus 303 via
an appropriate port interface. In a networked environment,
application programs 316 or program data 318 depicted relative to
computer system 300, or portions thereof, may be stored in a remote
memory storage device 340.
[0086] What have been described above are examples. It is, of
course, not possible to describe every conceivable combination of
components or methodologies, but one of ordinary skill in the art
will recognize that many further combinations and permutations are
possible. Accordingly, the invention is intended to embrace all
such alterations, modifications, and variations that fall within
the scope of this application, including the appended claims. For
example, while the foregoing source localization is disclosed in
the context of localizing a source of brain electrical activity, it
could be used for localizing a source of other types of electrical
activity, including other parts of the nervous system or muscular
system (e.g., muscles such as the heart or others).
[0087] As used herein, the term "includes" means includes but not
limited to, the term "including" means including but not limited
to. The term "based on" means based at least in part on.
Additionally, where the disclosure or claims recite "a," "an," "a
first," or "another" element, or the equivalent thereof, it should
be interpreted to include one or more than one such element,
neither requiring nor excluding two or more such elements.
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